Cloud condensation nuclei in polluted air and biomass burning smoke near the mega-city Guangzhou, China -Part 2: Size-resolved aerosol chemical composition, diurnal cycles, and externally mixed weakly CCN-active soot particles

Size-resolved chemical composition, mixing state, and cloud condensation nucleus (CCN) activity of aerosol particles in polluted mega-city air and biomass burning smoke were measured during the PRIDE-PRD2006 campaign near Guangzhou, China, using an aerosol mass spectrometer (AMS), a volatility tandem differential mobility analyzer (VTDMA), and a continuous-flow CCN counter (DMT-CCNC). The size-dependence and temporal variations of the effective average hygroscopicity parameter for CCN-active particles (kappa(a)) could be parameterized as a function of organic and inorganic mass fractions (f(org), f(inorg)) determined by the AMS: kappa(a,p) = kappa(org) . f(org) + kappa(inorg) . f(inorg). The characteristic kappa values of organic and inorganic components were similar to those observed in other continental regions of the world: kappa(org) approximate to 0.1 and kappa(inorg) approximate to 0.6. The campaign average kappa(a) values increased with particle size from similar to 0.25 at similar to 50 nm to similar to 0.4 at similar to 200 nm, while forg decreased with particle size. At similar to 50 nm, forg was on average 60% and increased to almost 100% during a biomass burning event. The VTDMA results and complementary aerosol optical data suggest that the large fractions of CCN-inactive particles observed at low supersaturations (up to 60% at S = 0.27%) were externally mixed weakly CCN-active soot particles with low volatility (diameter reduction < 5% at 300 degrees C) and effective hygroscopicity parameters around kappa(LV) approximate to 0.01. A proxy for the effective average hygroscopicity of the total ensemble of CCN-active particles including weakly CCNactive particles (kappa(t)) could be parameterized as a function of kappa(a, p) and the number fraction of low volatility particles determined by VTDMA (phi(LV)): kappa(t, p) = kappa(a, p)-phi(LV) . (kappa(a, p)-kappa(LV)). Based on kappa values derived from AMS and VTDMA data, the observed CCN number concentrations (NCCN, S = 10(2)-10(4) cm(-3) at S = 0.068-0.47%) could be efficiently predicted from the measured particle number size distribution. The mean relative deviations between observed and predicted CCN concentrations were similar to 10% when using kappa(t, p), and they increased to similar to 20% when using only kappa(a, p). The mean relative deviations were not higher (similar to 20%) when using an approximate continental average value of kappa approximate to 0.3, although the constant kappa value cannot account for the observed temporal variations in particle composition and mixing state (diurnal cycles and biomass burning events). Overall, the results confirm that on a global and climate modeling scale an average value of kappa approximate to 0.3 can be used for approximate predictions of CCN number concentrations in continental boundary layer air when aerosol size distribution data are available without information about chemical composition. Bulk or size-resolved data on aerosol chemical composition enable improved CCN predictions resolving regional and temporal variations, but the composition data need to be highly accurate and complemented by information about particle mixing state to achieve high precision (relative deviations < 20%).